def cifar10_iterator(batch_size,
data_shape,
resize=-1,
num_parts=1,
part_index=0):
get_data.GetCifar10()
train = mx.io.ImageRecordIter(
path_imgrec="data/cifar/train.rec",
# mean_img = "data/cifar/mean.bin",
resize=resize,
data_shape=data_shape,
batch_size=batch_size,
rand_crop=True,
rand_mirror=True,
num_parts=num_parts,
part_index=part_index)
val = mx.io.ImageRecordIter(
path_imgrec="data/cifar/test.rec",
# mean_img = "data/cifar/mean.bin",
resize=resize,
rand_crop=False,
rand_mirror=False,
data_shape=data_shape,
batch_size=batch_size,
num_parts=num_parts,
part_index=part_index)
return train, val
def cifar10(batch_size, input_shape, num_parts=1, part_index=0):
"""return cifar10 iterator"""
get_data.GetCifar10()
train = mx.io.ImageRecordIter(path_imgrec="data/cifar/train.rec",
mean_img="data/cifar/cifar_mean.bin",
data_shape=input_shape,
batch_size=batch_size,
rand_crop=False,
rand_mirror=False,
shuffle=False,
round_batch=False,
num_parts=num_parts,
part_index=part_index)
val = mx.io.ImageRecordIter(path_imgrec="data/cifar/test.rec",
mean_img="data/cifar/cifar_mean.bin",
rand_crop=False,
rand_mirror=False,
shuffle=False,
round_batch=False,
data_shape=input_shape,
batch_size=batch_size)
return (train, val)
def data(data_dir, batch_size, num_parts=1, part_index=0):
"""return cifar10 iterator"""
if data_dir == "data/cifar/":
sys.path.insert(0, "../../tests/python/common")
import get_data
get_data.GetCifar10()
input_shape = (3, 28, 28)
train = mx.io.ImageRecordIter(path_imgrec=data_dir + "train.rec",
mean_img=data_dir + "cifar_mean.bin",
data_shape=input_shape,
batch_size=batch_size,
rand_crop=True,
rand_mirror=True,
num_parts=num_parts,
part_index=part_index)
val = mx.io.ImageRecordIter(path_imgrec=data_dir + "test.rec",
mean_img=data_dir + "cifar_mean.bin",
rand_crop=False,
rand_mirror=False,
data_shape=input_shape,
batch_size=batch_size)
return (train, val)
in4c = SimpleFactory(in4b, 80, 80)
in4d = SimpleFactory(in4c, 48, 96)
in4e = DownsampleFactory(in4d, 96)
in5a = SimpleFactory(in4e, 176, 160)
in5b = SimpleFactory(in5a, 176, 160)
pool = mx.symbol.Pooling(data=in5b,
pool_type="avg",
kernel=(7, 7),
name="global_pool")
flatten = mx.symbol.Flatten(data=pool, name="flatten1")
fc = mx.symbol.FullyConnected(data=flatten, num_hidden=10, name="fc1")
softmax = mx.symbol.SoftmaxOutput(data=fc, name="loss")
#########################################################
get_data.GetCifar10()
batch_size = 128
num_epoch = 10
num_gpus = 1
train_dataiter = mx.io.ImageRecordIter(path_imgrec="data/cifar/train.rec",
mean_img="data/cifar/cifar_mean.bin",
rand_crop=True,
rand_mirror=True,
data_shape=(3, 28, 28),
batch_size=batch_size,
preprocess_threads=1,
label_name='loss_label')
test_dataiter = mx.io.ImageRecordIter(path_imgrec="data/cifar/test.rec",
mean_img="data/cifar/cifar_mean.bin",
rand_crop=False,
